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Review outlines core Bayesian concepts for stroke trials, comparing them to standard frequentist approaches.
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This review provides an introduction to Bayesian statistics for stroke researchers, explaining how it differs from frequentist approaches and how to conduct and interpret Bayesian analyses.
While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to frequentist approaches, which are based on the probability that data equal specific values given underlying unknown parameters, Bayesian approaches are based on the probability that parameters equal specific values given observed data and prior beliefs. The Bayesian paradigm allows researchers to update their beliefs with observed data to provide probabilistic interpretations of key parameters, for example, the probability that a treatment is effective. In this review, we outline the basic concepts of Bayesian statistics as they apply to stroke trials, compare them to the frequentist approach using exemplary data from a randomized trial, and explain how a Bayesian analysis is conducted and interpreted.
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Ospel et al. (2024) conducted a review in Stroke. Bayesian statistics vs. Frequentist statistics was evaluated. This review outlines the basic concepts of Bayesian statistics as they apply to stroke trials, comparing them to frequentist approaches and explaining how Bayesian analyses are conducted.
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